街角场景下考虑移动站与散射体速度变化的非稳定MIMO信道模型

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本文主要探讨了针对移动站（Mobile Station, MS）和散射体在街角场景中速度变化的非平稳多输入多输出（Multiple-Input Multiple-Output, MIMO）无线通信信道建模。传统的通信系统研究往往假设散射体固定不动，MS沿单一恒定速度运动，然而，现实环境中的无线传播条件更为复杂。随着移动互联网和物联网的发展，考虑动态因素对无线通信性能的影响变得尤为重要。在本文中，研究者Ji Bian、Yu Liu、Cheng-Xiang Wang、Jian Sun、Wensheng Zhang和Minggao Zhang合作，他们来自山东大学和赫里奥特瓦特大学，专注于无线通信技术和传感器信号系统领域。他们提出了一种新的非平稳MIMO信道模型，该模型突破了传统假设，不仅考虑了固定的散射体，还纳入了移动散射体和MS的速度随时间变化的特性。该模型的构建旨在更精确地模拟实际的无线信道行为，尤其是在街角这样复杂的电磁环境中，因为建筑物、行人和其他物体的移动会显著影响无线信号的传播路径和衰减。通过这种模型，研究人员可以更好地预测和分析在高速移动或动态散射环境下，MIMO系统的信道容量、信号质量以及可能的多径效应。这对于无线通信系统的设计优化、频谱效率提升以及抗干扰能力增强具有重要意义。此外，文中可能包含了对不同速度分布的统计分析，比如马尔科夫过程或其他随机过程，来描述散射体和MS的速度变化规律。同时，模型可能会包含空间-频率域的联合分析，以捕捉速度变化对多径传播特性的影响，这对于MIMO系统的波束赋形和信道编码等关键技术有指导作用。总结来说，这篇文章的研究成果对于无线通信领域的理论研究和工程实践具有重要的价值，它推动了无线通信模型的向前发展，使之更加接近真实世界的复杂性。

A Non-Stationary MIMO Channel Model for Street

Corner Scenarios Considering Velocity Variations of

the Mobile Station and Scatterers

Ji Bian

, Yu Liu

, Cheng-Xiang Wang

, Jian Sun

, Wensheng Zhang

, and Minggao Zhang

Shandong Provincial Key Lab of Wireless Communication Technologies,

School of Information Science and Engineering, Shandong University, Jinan, 250100, China.

Institute of Sensors, Signals and Systems, School of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, UK.

Email: {bianjimail, xinwenliuyu}@163.com, cheng-xiang.wang@hw.ac.uk, {sunjian, zhangwsh}@sdu.edu.cn, zmg225@163.com

Abstract—Most channel models in the literature assume that

the scatterers are ﬁxed and the mobile station (MS) moves

with a constant speed in a given direction. However, in realistic

propagation environments, both the scatterers and the MS can

be moving, and the velocities of the scatterers and the MS can

change with time. In this paper, we develop a non-stationary

multiple-input multiple-output (MIMO) channel model for street

corner scenarios. The proposed channel model takes into account

both ﬁxed and moving scatterers. Velocity variations, including

speed and movement direction, of the MS and moving scatterers

are considered. Analytical solutions of spatial cross-correlation

function (CCF), temporal autocorrelation function (ACF), and

Wigner-Ville spectrum are derived and analyzed. Moreover, the

impacts of velocity variations on the statistical properties of the

proposed model are investigated. The proposed channel model

is illuminating for future vehicle-to-infrastructure (V2I) and

vehicle-to-vehicle (V2V) channel modeling.

Index Terms—Non-stationary MIMO channel model, velocity

variations, time-variant parameters, statistical properties, street

corner scenarios.

I. INTRODUCTION

The V2V and V2I communications have drawn increasing

attention from researchers in recent years and are consid-

ered to play a crucial role in the next-generation intelligent

transportation systems (ITSs) [1], [2]. The V2V and V2I

communications are assumed to provide many beneﬁts, such

as reducing trafﬁc accidents and improving trafﬁc efﬁciency.

For example, in street corner scenarios, as illustrated in Fig. 1,

drivers usually have poor visibility of the road. Real-time road

condition information provided through extremely low latency

V2I/V2V communications can help to avoid a collision. For

system design and performance evaluation, detailed and accu-

rate knowledge of the channels for street corner scenarios is

necessary.

Most existing channel models are assumed to satisfy the

wide-sense stationary (WSS) assumption, which means the

stationarity of the channel during the observation time interval

is fulﬁlled in the wide sense [3]. However, channels in realistic

environments, especially in high mobility scenarios illustrate

non-stationary properties [4]–[7]. Authors in [8] reported that

the WSS assumption can result in erroneous evaluation of

system performance. Therefore, for high mobility scenarios,

the non-stationarity of channels in channel modeling has to

be considered. Most non-stationary channel models in the

literature were developed based on the movements of the MS

and clusters. Authors in [9] proposed a non-stationary IMT-

Advanced high-speed train (HST) channel model [10]. Time-

varying parameters including delays, powers, and angles were

calculated according to the movement of the receiver and

clusters. A three-dimensional (3-D) wideband non-stationary

geometry-based stochastic channel model (GBSM) for V2V

channels was proposed in [11]. The model is composed of

a line-of-sight (LoS) component, a two-sphere model, and an

elliptic-cylinder model. The last two models capture the effects

of moving vehicles and ﬁxed roadside scatterers, respectively.

The movements of the transmitter and receiver result in time-

varying angles of departure (AoDs) and time-varying angles of

arrival (AoAs), which can be obtained based on the geometric

construction.

A common assumption in the channel modeling is that

the MS moves along a straight line with a constant speed.

However, in real-world environments, the MS can move along

different trajectories with time-varying speed. Relaxing the

constant velocity assumption can help to develop more realistic

non-stationary channel models. In [12], a non-stationary chan-

nel model for isotropic scattering environments considering

velocity variations of the MS was proposed. The temporal

ACF and Wigner-Ville spectrum of the proposed model were

derived. In [13], a non-stationary mobile-to-mobile (M2M)

channel model allowing for velocity variations of the MS was

proposed. The authors developed the model under isotropic

scattering condition and illustrated that the correlation prop-

erties of the M2M channel can be signiﬁcantly affected by

the variations of the MS velocity. Further extension of [13]

were reported in [14], where the correlation properties of the

proposed model were investigated under non-isotropic scatter-

ing condition. However, models in [13], [14] are single-input

single-output (SISO) M2M channel models and the models

in [12]–[14] omitted the moving scatterers in propagation

environments.

In this paper, a non-stationary MIMO channel model for

street corner scenarios is developed using a geometry-based

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